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How can large software projects become more exploratory? This talk will show how dev-test cycles can be greatly reduced by diagnosing faults and predicting relevant tests using cutting edge AI and Machine Learning techniques. It presents how Siemens Healthineers and Codemanufaktur GmbH apply these techniques to preprocessed data from a Siemens Healthineers project to find fault causing changes faster and even prevent faults because the most relevant tests could be found before integration.

Extended AbstractFrom seeing a defect to knowing the fault is usually a cumbersome and long path in large software projects. Likewise, changes can lead to faults that often are only seen very late because running all tests takes a long time. Tools that automate the tasks of identifying faults as well as predicting relevant tests from changes can hence greatly reduce that dev-test cycle, thereby freeing up time for e.g. exploratory testing.Solving these problems based on the huge data amounts available in state-of-the-art development projects requires to address several tasks like preprocessing the data, transform it into a common language, postprocess it to make an analysis possible and make it efficient to add new incoming data.Together with Siemens Healthineers a framework was developed by Codemanufaktur GmbH to support the mentioned points. It can access data from e.g. Team Foundation Server or DOORS, preprocess it into a traceML based model which is stored in a graph database and then can be further used to build a probabilistic model on this “data lake”.The prerequisites for such an analysis are shown and how the mentioned approach helped at Siemens Healthineers to improve the feedback cycles as fault causing changes have been found faster or even faults could be prevented because the most relevant tests could be found before the changes were integrated.

How to stop hating your UI tests

Test automation projects can have a bad tendency to go awry, most especially when higher-level automation (e.g. via the UI) is involved. At some point, automated tests just become too hard to understand, extend and maintain.In this talk, I’ll present a structured, systematic and tool-independent approach for automating UI tests.I’ll show examples to illustrate the structures I describe. Anyone involved in automating tests (whether they are programmers or not) can profit from learning and applying these patterns in their teams.

Extended AbstractTest automation projects can have a bad tendency to go awry, most especially when higher-level automation (e.g. via the UI) is involved. At some point, automated tests just become too hard to understand, extend and maintain.This doesn’t have to be the case though. In software development, there are systematic methods and patterns for addressing recurring challenges – and similar approaches also exist for test automation.In this talk, I’ll present a structured, systematic and tool-independent approach for automating UI tests. The approach and the patterns that result from it haven’t simply been invented from scratch, rather they build on and expand patterns and methodologies well-known from software development and web-testing for example.During the talk, I’ll show examples to illustrate the structures I describe. Anyone involved in automating tests (whether they are programmers or not) can profit from learning and applying these patterns in their teams.